DARPA applies Big Data to debugging

Mar. 27, 2014 - 12:40PM
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DARPA wants to employ Big Data techniques to eliminate software errors and bad coding. The project, known as Mining and Understanding Software Enclaves (MUSE), would develop tools to automatically detect and repair errors, according to the DARPA announcement.

"Unfortunately, in spite of developers’ best efforts, software errors are at the root of most execution errors and security vulnerabilities," notes DARPA, which wants to create "a community infrastructure that would incorporate a continuously operational specification-mining engine.”

The engine would use deep program analyses and foundational ideas underlying Big Data analytics to maintain a database containing inferences about the properties, behaviors and vulnerabilities of the program components.

“If successful, MUSE could provide numerous capabilities that have so far remained elusive," reads the announcement.

Suresh Jagannathan, DARPA's program manager for MUSE, envisions using Big Data techniques to improve the hundreds of billions of lines of open source code in use today.

“Ideally, we could enable a paradigm shift in the way we think about software construction and maintenance, replacing the existing costly and laborious test/debug/validate cycle with ‘always on’ program analysis, mining, inspection and discovery,” Jagannathan said. “We could see scalable automated mechanisms to identify and repair program errors, as well as tools to efficiently create new, custom programs from existing components based only a description of desired properties.”